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The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and applications. Point cloud completion techniques, by predicting and filling these missing regions, are vital for restoring the integrity of cultural heritage structures, enhancing restoration accuracy and efficiency. In this paper, for challenges in processing large-scale cultural heritage point clouds, particularly the slow processing speed and visualization impairments from uneven point density during completion, we propose a point cloud completion employing centroid-based voxel feature extraction, which significantly accelerates feature extraction for massive point clouds. Coupled with an efficient upsampling module, it achieves a uniform point distribution. Experimental results show that the proposed method matches SOTA performance in completion accuracy while surpassing in point density uniformity, demonstrating capability in handling larger-scale point cloud data, and accelerating the processing of voluminous point clouds. In general, the proposed method markedly enhances the efficiency and quality of large-scale point cloud completion, holding significant value for the digital preservation and restoration of cultural heritage.
Li et al. (Sun,) studied this question.